How Much Does Nano Banana 2 Image Generation Cost?

Nano Banana 2 billing starts with image tokens, but the image price is not necessarily the entire request cost. A saved Ofox response for a 1024×1024 image reports 1,120 image tokens. At the September 7 catalog rate of $60 per million, that component costs $0.0672. Input and any separately billed output must also be checked.

This guide is for developers estimating image API costs. We reviewed the saved September 7, 2026 responses and test logs on September 8. Gateway observations below apply to that route and sample; they are not model-wide guarantees.

Nano Banana 2 image-token pricing

These are Google’s standard image-output tiers, checked against its Gemini pricing table. They exclude other request components and should not be mixed with batch or priority prices. The saved Ofox 1K, 2K and 4K PNGs matched their requested dimensions and the token counts above.

For current route details, check the Nano Banana 2 model page. The Lite model had a recorded image-output rate of $30/M, giving a $0.0336 image component for 1,120 tokens. Compare output suitability and complete billing before switching models for cost.

Thinking costs: a missing field is not zero cost

Nano Banana 2 supports thinking. Google’s image generation guide documents thinking for Gemini 3 image models and configurable thinking levels for Flash Image. Therefore, an Ofox response without reasoning_tokens cannot establish that the underlying model does not think or that every route makes thinking free.

The saved image responses exposed output_tokens_details.image_tokens. Separate chat responses for prompts counted as 8 and 51 input tokens both reported 1,120 completion tokens, but did not expose the same image-token breakdown. The original article conflated these response formats. Neither pair proves that all prompt lengths have identical total costs.

For Nano Banana Pro, the original test log recorded:

The calculation uses $120/M for image output and $12/M for reasoning. Input is excluded. The longer-prompt JSON remains available and confirms 377 reasoning tokens; the 223 result is preserved in the test log. Two different prompts cannot establish a general relationship between prompt length and reasoning usage.

Pro’s separate thinking rate is documented in Google’s pricing table. The previous title’s claim that nobody documents it was incorrect.

The 512px request mismatch on Ofox

The saved response to the request labelled 512x512 contained a 1024×1024 PNG, with 1,120 image tokens. We decoded the base64 image and checked its PNG header again during this review.

Relative to 747 tokens, 1,120 is about 1.50 times the image-output cost. This is a gateway size-mapping observation, not evidence that Google lacks a 512px tier. Before budgeting smaller drafts, verify your provider’s supported request fields, the returned dimensions and the billed usage.

Inspect the response before scaling

This is the Ofox request shape used for the 1K image test:

curl -X POST https://api.ofox.run/v1/images/generations \
  -H "Authorization: Bearer $OFOX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-3.1-flash-image",
    "prompt": "A single red apple on a white table",
    "size": "1024x1024"
  }'

Check that data[0].b64_json contains a decodable image, then inspect usage and compare it with your account’s billing record. Usage counters support a cost estimate; they are not themselves an invoice.

The saved chat-route samples returned completion usage without an image. On this tested integration, use the images route and validate the payload instead of treating HTTP 200 as proof of successful image delivery. This finding does not describe Google’s native API or every OpenAI-compatible provider.

The original test also reported an upstream error when n exceeded one. Treat that as an integration limitation. Google’s separate Batch API is documented in its image generation guide.

A practical billing checklist

• Record provider, model ID, endpoint, requested size and actual dimensions.
• Separate image output, input and any text or reasoning components.
• Pin supported parameters and compare the estimate with a billed request.
• Recheck route behavior before increasing volume; do not extrapolate a handful of calls to every prompt.

Related

• GPT Image 2 quality and latency — compare quality tiers with explicit size and cost assumptions.
• GPT Image 2 generation failures — diagnose failed image requests.
• Qwen 3.8 Flash reasoning-token billing — another example of separating output components.

Correction, September 8, 2026: removed claims that Nano Banana 2 has no thinking costs, that Pro thinking charges are undocumented, and that two prompt samples establish invariant billing. The 512px finding is scoped to the tested Ofox route. This review used saved responses and logs; it did not run new paid requests.